One of the last technical challenges of cloud adoption is right security configuration. This session focuses on Azure Sql PaaS, covers governance, risk management and compliance and provides 8-step process for securing public cloud.
This talk will address how to add the unit testing framework tSQLt to the database deployment pipeline. The purpose is to reduce the cost of validate every change in the database with a fully automated pipeline.
We introduce the concept of aggregation, we show several examples of their usage understanding the advantages and the limitations of aggregations, with the goal of building a solid understanding on how and when to use the feature in data models.
Within Azure we have a rich ecosystem of AI services that can be leveraged to gain new insights into your data. This session will give you an easy to digest breakdown of the key services that matter and how to approach each one. Cognitive Services, Bot Framework, Azure Machine Learning Studio, Databricks, Notebooks, the Azure ML SDK for Python and the Azure ML Service
This session will take a look at better Unicode support, query processing improvements for row store tables, secure enclaves, and other neat things you'll find useful as a modern database administrator or developer.
A walk-through on what is possible analyzing your data with the "R" language.
Tabular Editor, an open source tool for authoring Tabular Models, makes it easier for teams of developers to work on the same model simultaneously. It also provides functionality for automated build and deployment. In short, DevOps for SSAS Tabular.
Azure offers a comprehensive set of big-data solutions that help you gather, store, process, analyse and visualise data of any variety, volume or velocity, so you can discover new opportunities and take quick action. In this overview session, we’ll look at the various components within Azure that make up the Modern Data Warehouse, enable Real-Time Analytics, and support Advanced Analytics scenarios. You should leave with a high level understanding of the capabilities and limitations of each of the products within the Azure Analytics portfolio.
We will review this new feature of ADFv2, do deep dive to understand the mentioned techniques, compare them to SSIS and/or T-SQL and learn how modelled data flow runs Scala behind the scenes.
Azure Databricks support both Classic and Deep Learning ML Algorithms to analyse Large DataSets at scale. The Integrated Notebook experience gives the Data Scientists and Data Engineers to do exploratory Data Analysis, also feels like native to Jupyter notebook users. In this session we will extract intelligence from Higgs Dataset (Particle Physics) by running Classic and Deep Learning models using Azure Databricks. We will also peek into AMl service's integration with Azure Databricks for managing the end-to-end machine learning lifecycle.
Managed Instances can make your cloud migrations simpler, but have their own nuances. Learn about what you need to know to manage this new platform.
Biml is not just for generating SSIS packages! Come and see how you can use Biml to save time and speed up other Data Warehouse development tasks like T-SQL development, test data creation, and dimension population.
In this session, we’ll look at the different options within the Cognitive Services suite, show you how to connect to the APIs using Python code, walk through a live bot demo, and build an Azure Cognitive Search index. You should leave this session feeling like you’ve had a jump start to further your AI developer skill set.
This session looks at creating a SQL test lab on your workstation. We start by selecting a hypervisor, look at building a virtual machine and then creating a domain controller, a Windows failover cluster and a couple of SQL Servers.
Based on real life scenarios, an audience interactive session.
Learning DAX can be tricky, especially if you have a background in SQL.
In this session we'll look at ETL metadata, use it to drive process execution, and see benefits quickly emerge. I'll show how a metadata-first approach reduces complexity, enhances resilience and allows ETL processing to become self-organising.
In this hour long session we will attempt to include lots of advice and guidance on how to develop code that will easily get approved by your DBA prior to release to production.
Using Azure DevOps and Azure RM templates to created isolated environments for testing PaaS solutions.
Power BI Premium and Analysis Services enable you to build comprehensive, enterprise-scale analytic solutions. This session will deep dive into exciting new and upcoming features.
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